Optimizing database queries having hierarchy filters

ABSTRACT

A query is received by a database server from a remote application server. The query is associated with a calculation scenario that defines a data flow model that includes one or more calculation nodes and that includes at least one hierarchy filter. Each hierarchy filter is a filter of at least one hierarchy table. Each calculation node defines one or more operations to execute on the database server. Thereafter, the database server instantiates the calculation scenario. The instantiating includes optimizing the calculation scenario by transforming at least one hierarchy filter and pushing down the at least one transformed hierarchy filter to a lower calculation node. The database server then executes the operations defined by the calculation nodes of the optimized calculation scenario to result in a responsive data set. The database server can subsequently provide the data set to the application server.

TECHNICAL FIELD

The subject matter described herein relates to optimizing queries ofhierarchical data for calculation scenarios executed by a calculationengine.

BACKGROUND

Data flow between an application server and a database server is largelydependent on the scope and number of queries generated by theapplication server. Complex calculations can involve numerous queries ofthe database server which in turn can consume significant resources inconnection with data transport as well as application server-sideprocessing of transported data. Calculation engines can sometimes beemployed by applications and/or domain specific languages in order toeffect such calculations. Such calculation engines can executecalculation models/scenarios that comprise a plurality of hierarchicalcalculation nodes. In many cases, pushing down operations to nodes lowerin a hierarchy can increase performance.

SUMMARY

In one aspect, a query is received by a database server from a remoteapplication server. The query is associated with a calculation scenariothat defines a data flow model that includes one or more calculationnodes and that includes at least one hierarchy filter. Each hierarchyfilter is a filter of at least one hierarchy table. Each calculationnode defines one or more operations to execute on the database server.Thereafter, the database server instantiates the calculation scenario.The instantiating includes optimizing the calculation scenario bytransforming at least one hierarchy filter and pushing down the at leastone transformed hierarchy filter to a lower calculation node. Thedatabase server then executes the operations defined by the calculationnodes of the optimized calculation scenario to result in a responsivedata set. The database server can subsequently provide the data set tothe application server.

The transforming can include transforming the at least one hierarchyfilter to an inlist filter. The inlist filter can restrict values of afirst join attribute to values of a second join attribute afterexecuting one join partner.

The transforming can include transforming each hierarchy filter to aseparate inlist filter by extracting corresponding data of acorresponding hierarchy table and formulating an inlist query on a joinattribute provided by a composite data provider.

At least a portion of paths and/or attributes defined by the calculationscenario can, in some implementations, not be required to respond to thequery. In such cases, the instantiated calculation scenario can omit thepaths and attributes defined by the calculation scenario that are notrequired to respond to the query.

At least one of the calculation nodes can filter results obtained fromthe database server. At least one of the calculation nodes can sortresults obtained from the database server.

The calculation scenario can be instantiated in a calculation enginelayer by a calculation engine. The calculation engine layer can interactwith a physical table pool and a logical layer. The physical table poolcan include physical tables containing data to be queried, and thelogical layer can define a logical metamodel joining at least a portionof the physical tables in the physical table pool. The calculationengine can invoke an SQL processor for executing set operations.

An input for each calculation node can include one or more of: aphysical index, a join index, an OLAP index, and another calculationnode. Some or all calculation nodes can have at least one output tablethat is used to generate the data set. At least one calculation node canconsume an output table of another calculation node.

The query can be forwarded to a calculation node in the calculationscenario that is identified as a default node if the query does notspecify a calculation node at which the query should be executed. Thecalculation scenario can include database metadata.

The database can be a column oriented database. The database can be anin-memory database.

Computer program products are also described that comprisenon-transitory computer readable media storing instructions, which whenexecuted one or more data processors of one or more computing systems,causes at least one data processor to perform operations herein.Similarly, computer systems are also described that may include one ormore data processors and a memory coupled to the one or more dataprocessors. The memory may temporarily or permanently store instructionsthat cause at least one processor to perform one or more of theoperations described herein. In addition, methods can be implemented byone or more data processors either within a single computing system ordistributed among two or more computing systems. Such computing systemscan be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including but notlimited to a connection over a network (e.g. the Internet, a wirelesswide area network, a local area network, a wide area network, a wirednetwork, or the like), via a direct connection between one or more ofthe multiple computing systems, etc.

The subject matter described herein provides many advantages. Forexample, the current subject provides optimizes hierarchical queries oncomposite data providers so that they can provide reduced query responsetimes, while consuming lower amounts of memory. These advances areparticularly effective with regard to big data scenarios.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a process flow diagram illustrating a method for optimizinghierarchical filters in a database;

FIG. 2 is a diagram illustrating a calculation engine layer, a logicallayer, a physical table pool and their interrelationship;

FIG. 3 is a diagram illustrating an architecture for processing andexecution control;

FIG. 4 is a diagram illustrating a stored calculation model foraggregation on top of a table;

FIG. 5 is a diagram illustrating an executed calculation modelcorresponding to the stored calculation model of FIG. 4;

FIG. 6 is a diagram illustrating a stored calculation model for a joinon top of tables;

FIG. 7 is a diagram illustrating an executed calculation modelcorresponding to the stored calculation model of FIG. 6;

FIG. 8 is a first diagram illustrating a stored calculation model for anaggregation operation;

FIG. 9 is a first diagram illustrating an executed calculation modelcorresponding to the stored calculation model of FIG. 8;

FIG. 10 is a second diagram illustrating an executed calculation modelcorresponding to the stored calculation model of FIG. 8;

FIG. 11 is a diagram illustrating a business warehouse hierarchy; and

FIG. 12 is a calculation engine executing a query of a composite dataprovider deriving from a business warehouse application layer.

DETAILED DESCRIPTION

FIG. 1 is a process flow diagram 100 illustrating a method in which, at110, a query is received by a database server from a remote applicationserver that is associated with a calculation scenario that defines adata flow model including one or more calculation nodes and thatincludes at least one hierarchy filter. Each hierarchy filter being afilter of at least one hierarchy table. Each calculation node definesone or more operations to execute on the database server. Subsequently,at 120, the database server optimizes and instantiates the calculationscenario by transforming at least one hierarchy filter and pushing downthe at least one transformed hierarchy filter to a lower calculationnode. Thereafter, at 130, the operations defined by the calculationnodes of the instantiated calculation scenario can be executed to resultin a responsive data set. Next, at 140, the data set is provided to theapplication server by the database server.

FIG. 2 is a diagram 200 that illustrates a database system in whichthere are three layers, a calculation engine layer 210, a logical layer220, and a physical table-pool 230. Calculation scenarios can beexecuted by a calculation engine which can form part of a database orwhich can be part of the calculation engine layer 210 (which isassociated with the database). The calculation engine layer 210 can bebased on and/or interact with the other two layers, the logical layer220 and the physical table pool 230. The basis of the physical tablepool 230 consists of physical tables (called indexes) containing thedata. Various tables can then be joined using logical metamodels definedby the logical layer 220 to form a new index. For example, the tables ina cube (OLAP view) can be assigned roles (e.g., fact or dimensiontables) and joined to form a star schema. It is also possible to formjoin indexes, which can act like database view in environments such asthe Fast Search Infrastructure (FSI) by SAP AG.

As stated above, calculation scenarios can include individualcalculation nodes 211-214, which in turn each define operations such asjoining various physical or logical indexes and other calculation nodes(e.g., CView 4 is a join of CView 2 and CView 3). That is, the input fora calculation node 211-214 can be one or more physical, join, or OLAPviews or calculation nodes. A calculation node as used herein representsa operation such as a projection, aggregation, join, union, minus,intersection, and the like. Additionally, as described below, inaddition to a specified operation, calculation nodes can sometimes beenhanced by filtering and/or sorting criteria. In some implementations,calculated attributes can also be added to calculation nodes.

In calculation scenarios, two different representations can be provided.First, a stored (“pure”) calculation scenario in which all possibleattributes are given. Second, an instantiated/executed model thatcontains only the attributes requested in the query (and required forfurther calculations). Thus, calculation scenarios can be created thatcan be used for various queries. With such an arrangement, calculationscenarios can be created which can be reused by multiple queries even ifsuch queries do not require every attribute specified by the calculationscenario. For on-the-fly scenarios this means that the same calculationscenario (e.g., in XML format, etc.) can be used for different queriesand sent with the actual query. The benefit is that on applicationserver side the XML description of a calculation scenario can be usedfor several queries and thus not for each possible query one XML has tobe stored.

Further details regarding calculation engine architecture andcalculation scenarios can be found in U.S. Pat. No. 8,195,643, thecontents of which are hereby fully incorporated by reference.

FIG. 3 is a diagram 300 illustrating a sample architecture for requestprocessing and execution control. As shown in FIG. 3, artifacts 305 indifferent domain specific languages can be translated by their specificcompilers 310 into a common representation called a “calculationscenario” 315 (illustrated as a calculation model). To achieve enhancedperformance, the models and programs written in these languages areexecuted inside the database server. This arrangement eliminates theneed to transfer large amounts of data between the database server andthe client application. Once the different artifacts 305 are compiledinto this calculation scenario 315, they can be processed and executedin the same manner. The execution of the calculation scenarios 315 isthe task of a calculation engine 320.

The calculation scenario 315 can be a directed acyclic graph with arrowsrepresenting data flows and nodes that represent operations. Eachcalculation node has a set of inputs and outputs and an operation thattransforms the inputs into the outputs. In addition to their primaryoperation, each calculation node can also have a filter condition forfiltering the result set. The inputs and the outputs of the operationscan be table valued parameters (i.e., user-defined table types that arepassed into a procedure or function and provide an efficient way to passmultiple rows of data to the application server). Inputs can beconnected to tables or to the outputs of other calculation nodes.Calculation scenarios 315 can support a variety of node types such as(i) nodes for set operations such as projection, aggregation, join,union, minus, intersection, and (ii) SQL nodes that execute a SQLstatement which is an attribute of the node. In addition, to enableparallel execution, a calculation scenario 315 can contain split andmerge operations. A split operation can be used to partition inputtables for subsequent processing steps based on partitioning criteria.Operations between the split and merge operation can then be executed inparallel for the different partitions. Parallel execution can also beperformed without split and merge operation such that all nodes on onelevel can be executed in parallel until the next synchronization point.Split and merge allows for enhanced/automatically generatedparallelization. If a user knows that the operations between the splitand merge can work on portioned data without changing the result he orshe can use a split. Then, the nodes can be automatically multipliedbetween split and merge and partition the data.

Calculation scenarios 315 are more powerful than traditional SQL queriesor SQL views for many reasons. One reason is the possibility to defineparameterized calculation schemas that are specialized when the actualquery is issued. Unlike a SQL view, a calculation scenario 315 does notdescribe the actual query to be executed. Rather, it describes thestructure of the calculation. Further information is supplied when thecalculation scenario is executed. This further information can includeparameters that represent values (for example in filter conditions). Toobtain more flexibility, it is also possible to refine the operationswhen the model is invoked. For example, at definition time, thecalculation scenario 315 may contain an aggregation node containing allattributes. Later, the attributes for grouping can be supplied with thequery. This allows having a predefined generic aggregation, with theactual aggregation dimensions supplied at invocation time. Thecalculation engine 320 can use the actual parameters, attribute list,grouping attributes, and the like supplied with the invocation toinstantiate a query specific calculation scenario 315. This instantiatedcalculation scenario 315 is optimized for the actual query and does notcontain attributes, nodes or data flows that are not needed for thespecific invocation.

When the calculation engine 320 gets a request to execute a calculationscenario 315, it can first optimize the calculation scenario 315 using arule based model optimizer 322. Examples for optimizations performed bythe model optimizer can include “pushing down” filters and projectionsso that intermediate results 326 are narrowed down earlier, or thecombination of multiple aggregation and join operations into one node.The optimized model can then be executed by a calculation engine modelexecutor 324 (a similar or the same model executor can be used by thedatabase directly in some cases). This includes decisions about parallelexecution of operations in the calculation scenario 315. The modelexecutor 324 can invoke the required operators (using, for example, acalculation engine operators module 328) and manage intermediateresults. Most of the operators are executed directly in the calculationengine 320 (e.g., creating the union of several intermediate results).The remaining nodes of the calculation scenario 315 (not implemented inthe calculation engine 320) can be transformed by the model executor 324into a set of logical database execution plans. Multiple set operationnodes can be combined into one logical database execution plan ifpossible.

The model optimizer 322 can be configured to enable dynamic partitioningbased on one or more aspects of a query and/or datasets used by queries.The model optimizer can implement a series of rules that are triggeredbased on attributes of incoming datasets exceeding specified thresholds.Such rules can, for example, apply thresholds each with a correspondinga parallelization factor. For example, if the incoming dataset has 1million rows then two partitions (e.g., parallel jobs, etc.) can beimplemented, or if the incoming dataset has five million rows then fivepartitions (e.g., parallel jobs, etc.) can be implemented, and the like.

The attributes of the incoming datasets utilized by the rules of modeloptimizer 322 can additionally or alternatively be based on an estimatedand/or actual amount of memory consumed by the dataset, a number of rowsand/or columns in the dataset, and the number of cell values for thedataset, and the like.

The calculation engine 320 typically does not behave in a relationalmanner. The main reason for this is the instantiation process. Theinstantiation process can transform a stored calculation model 315 to anexecuted calculation model 315 based on a query on top of a calculationview which is a (catalog) column view referencing one specific node of astored calculation model 315. Therefore, the instantiation process cancombine the query and the stored calculation model and build theexecuted calculation model.

The main difference between a relational view or SQL with subselects anda calculation model is that the projection list in a relational view isstable also if another SQL statement is stacked on top whereas in acalculation model the projection list of each calculation node in thecalculation model is depending on the projection list of the query orthe parent calculation node(s).

With a calculation model 315, a user can provide a set ofattributes/columns on each calculation node that can be used by the nextcalculation node or the query. If attributes/columns are projected in aquery or on the parent calculation node, then just a subset of theserequested attributes/columns can be considered in the executedcalculation model.

The following examples (with reference to FIGS. 4-10) illustrate thedifference between a stored calculation model and an executedcalculation model:

FIG. 4 shows a stored calculation model 400 for an aggregation on top ofa table that is a sum of K1 and K2 across three different views A, B, C.FIG. 5 shows an executed calculation model 500 and query thatcorresponds to the stored calculation model 400 of FIG. 4 in which in afirst table calculation node only the attributes/columns A, K1 are inthe projection list and in a second table calculation node, only theattributes/columns A, B, C, K2 are in the projection list.

FIG. 6 shows a stored calculation model 600 for a join on top of theillustrated tables. FIG. 7 shows an executed calculation model 700 andquery that corresponds to the stored calculation model 600 of FIG. 6 inwhich the attribute A is added to the projection list at a join node andthe underlying nodes (because such an arrangement is required forjoins).

FIG. 8 shows a stored calculation model 800 for aggregation. With thisstored calculation model 800, semantics can change depending on theattributes/columns requested by the calculation engine 320. Further,with the stored calculation model 800 of FIG. 8, the aggregationcalculation node defines a calculated attribute. FIG. 9 shows a firstexecuted calculation model 900 and query that corresponds to the storedcalculation model 800 of FIG. 8 in which the aggregation over the tableattribute A is added to a projection list due to the calculatedattribute C_Attr. FIG. 10 shows a second executed calculation model 1000and query that corresponds to the stored calculation model 800 of FIG. 8and in which the Attribute A is not added to the projection list of theaggregation (because the calculated attribute C_Attr. was not queried).

Data warehouse platforms such as the SAP Business Warehouse (BW) canoffer tools to create new data providers from two or more data sources(referred to herein as composite data providers). The data sources caninclude, for example, objects such as infoproviders (e.g. OLAP Views,etc.), tables, multiproviders, data store objects, and transientinfoproviders (e.g., SAP HANA views, etc.). In order to achieve betterquery response times, while taking advantage of the relatively lowmemory consumption of such composite data providers, complexcalculations can be pushed into the calculation engine 320 which can usethe calculation view like all other objects.

Certain business warehouse applications (e.g., SAP BW applications,etc.) can enrich business warehouse data models with hierarchicalinformation. Hierarchical information can, in turn, allow users to buildup hierarchical representations of data, relationships between differentinfoobjects and enable them to report and navigate on hierarchicalstructured data. An example of a business warehouse hierarchy can beseen in diagram 1100 of FIG. 11.

Hierarchical data can be processed on the application level and it canbe pushed down into the database by using the calculation engine 320. Inaddition, the calculation engine 320 can push down performance relevantsteps of hierarchical queries into lower engine layers itself (e.g., theOLAP engine layer 220) in order to reduce response times and resourceconsumption of such queries. To effect the same, the calculation engine320 can provide special operators and interfaces which can be used by anapplication (e.g., a business warehouse application, etc.) to define sohierarchy tables which representing certain relationships betweeninfoobjects and hierarchy information (e.g., Table 1 below). Please notethat all semantically information on hierarchies still is kept by the BWapplications.

TABLE 1 Hierarchy H000 pred succ 1 1 −2 3 −4 5 −9 5 −1 1 −7 1 −3 4 3 2

In order to enhance query results of composite data providers withhierarchical information, the calculation engine 320 can provide aspecial request operation which inner joins hierarchy tables to thefinal result, filters and aggregates the result within one singleoperation step as illustrated in diagram 1200 of FIG. 12. In particular,FIG. 12 illustrates a query of a composite data provider 1230 thatoriginates from an application layer 1210. The calculation engine 320optimizes the query via at least one hierarchy operation 1220 (which isdescribed in further detail below).

In some cases, users might restrict analytical queries on specifichierarchy levels so that queries on composite data providers areenhanced by additional filter conditions on hierarchy table columns.Given a calculation view CALCVIEW, a joined hierarchy table H000 and ahierarchy column succ, corresponding queries can be represented asfollows:

SELECT SUM(SALES), REGION FROM CALCVIEW WHERE REGION=‘US’ ANDH000.succ=5 GROUP BY REGION

Even for simple data providers (e.g., business warehouse data providers,etc.), hierarchy processing as provided herein can lead to bigimprovements in execution time and memory consumption as hierarchy joinsand hierarchy filters usually restrict result sets significantly inregard of row number and memory consumption. If the calculation engineoptimizer 322 is able to push all performance critical processing stepslike filtering, aggregating and hierarchy joining into single planoperations, the need to materialize intermediate results betweendifferent calculation engine operators 328 can be minimized or even beavoided.

However especially for complex composite data providers, such asillustrated in diagram 1200, optimizations are often limited because ofthe requirement (from a semantic perspective) to materializeintermediate results between different nodes/operations. As hierarchyjoins are always processed as a final processing step, intermediateresults of previous operators can still contain big amounts of data andtherefore still might harm overall query performance.

Furthermore, hierarchy filters often harm other optimizations such aspush downs of filter conditions. Given a filter conditions like WHEREPRODUCT=‘Car’ OR H000.succ=5, a push down of filter PRODUCT cannot bedone without changing the query semantics. As fems (“Formel ElementSelektion”/“selection for formula element”) filters (which in a way canbe seen as or filter conditions) are likely used in BW queries,hierarchy filters often prevent other filter optimizations from beingfully realized.

The current subject matter overcomes the problems described above byproviding a new optimization pattern of hierarchical queries oncomposite data providers that, in turn, can provide reduced responsetimes as well as lower memory consumption especially for big datascenarios.

A new filter optimization of hierarchy filters can be implemented asdescribed herein that provides a transformation of filters on hierarchytables to filter conditions which can be further processed and pusheddown by existing calculation engine 322 optimizer logic. Filterconditions on hierarchy attributes can be transformed to special inlistfilters which then can be pushed down and optimized by underlyingcalculation nodes in the process. Given a join with two join inputs Aand B, inner joins can be optimized using inlist filters which restrictvalues of one join attribute of join partner A to values of the otherjoin attribute of join partner B after executing B. As the calculationengine optimizer 322 can have knowledge about hierarchy joins and alsois able to query and extract data of database tables 231-234 duringoptimization, each hierarchy filter can be expressed as a separateinlist filter by extracting the corresponding data of the hierarchytable and formulating an inlist query on the join attribute provided bythe composite data provider. For example, a query includes a hierarchyfilter on “city” with cities from south Germany. Thus, the hierarchytable can be queried and the result can be transformed into thecorresponding inlist filter which could be further optimized (pusheddown) in the calculation model. Such an inlist filter could look be asfollows (pseudo SQL): . . . WHERE “city” in (“Walldorf”, “Heidelberg”,“Frankfurt”).

Given the hierarchy table H000 of Table 1 and two join attributesH000.pred and LOCATION_SID the initial query can formulated as:

-   -   SELECT SUM(SALES), REGION FROM CALCVIEW    -   WHERE REGION=‘US’ AND LOCATION_SID IN=(−4,−9) GROUP BY REGION

Once a hierarchy filter is converted, it can be pushed down within thecalculation model as any other filter condition so that it can restrictintermediate result sizes of previous operations during execution. Asinlist filters based on hierarchy filters are usually very restrictive,a filter push down to a data source can provide significant improvementsin performance and memory consumption. Even if hierarchy attributes arerequested without additional filter conditions this approach can beapplied. In this case, join attributes of the composite provider simplycan be restricted to all values of the hierarchy table join attribute.

The current optimization techniques can also allow removal of hierarchyjoins completely from the execution model. As hierarchy joins aredefined as inner joins with 1:1 cardinality, hierarchy joins can beremoved if all filter conditions on this hierarchy are successfullytranslated into inlists and no attribute of the hierarchy table isrequested. Due to the cardinality knowledge, it can be ensured that nojoin can ever increase query results and therefore it can safely beremoved as all join semantics are already provided by the correspondinginlist filters. Thus, removing unnecessary joins can reduce complexityfor such queries in order to further improve performance and memoryconsumption in big data scenarios.

One or more aspects or features of the subject matter described hereinmay be realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations may include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device (e.g., mouse, touch screen, etc.), andat least one output device.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural language, an object-orientedprogramming language, a functional programming language, a logicalprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, the subject matter describedherein can be implemented on a computer having a display device, such asfor example a cathode ray tube (CRT) or a liquid crystal display (LCD)monitor for displaying information to the user and a keyboard and apointing device, such as for example a mouse or a trackball, by whichthe user may provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well. For example,feedback provided to the user can be any form of sensory feedback, suchas for example visual feedback, auditory feedback, or tactile feedback;and input from the user may be received in any form, including, but notlimited to, acoustic, speech, or tactile input. Other possible inputdevices include, but are not limited to, touch screens or othertouch-sensitive devices such as single or multi-point resistive orcapacitive trackpads, voice recognition hardware and software, opticalscanners, optical pointers, digital image capture devices and associatedinterpretation software, and the like.

The subject matter described herein may be implemented in a computingsystem that includes a back-end component (e.g., as a data server), orthat includes a middleware component (e.g., an application server), orthat includes a front-end component (e.g., a client computer having agraphical user interface or a Web browser through which a user mayinteract with an implementation of the subject matter described herein),or any combination of such back-end, middleware, or front-endcomponents. The components of the system may be interconnected by anyform or medium of digital data communication (e.g., a communicationnetwork). Examples of communication networks include a local areanetwork (“LAN”), a wide area network (“WAN”), and the Internet.

The computing system may include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flow(s) depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

What is claimed is:
 1. A method comprising: receiving, by a databaseserver from a remote application server, a query associated with acalculation scenario that defines a data flow model that includes one ormore calculation nodes and that includes at least one hierarchy filter,each hierarchy filter being a filter of at least one hierarchy table,each calculation node defining one or more operations to execute on thedatabase server; instantiating, by the database server, the calculationscenario, the instantiating optimizing the calculation scenario, theinstantiating comprising: transforming at least one hierarchy filter toan inlist filter, the inlist filter restricting values of a first joinattribute to values of a second join attribute after executing one joinpartner; and pushing down the inlist filter to a lower calculation node;executing, by the database server, the operations defined by thecalculation nodes of the optimized calculation scenario to result in aresponsive data set; and providing, by the database server to theapplication server, the data set.
 2. A method as in claim 1, wherein thetransforming comprises: transforming each hierarchy filter to a separateinlist filter by extracting corresponding data of a correspondinghierarchy table and formulating an inlist query on a join attributeprovided by a composite data provider.
 3. A method as in claim 1,wherein the instantiating further comprises: removing at least onehierarchy join from the calculation scenario.
 4. A method as in claim 3,wherein the at least one hierarchy join is removed from the calculationscenario if all filter conditions are transformed and no attribute of acorresponding hierarchy table is requested.
 5. A method as in claim 1,wherein the database server comprises a column-oriented database.
 6. Amethod as in claim 1, wherein at least a portion of paths and/orattributes defined by the calculation scenario are not required torespond to the query, and wherein the instantiated calculation scenarioomits the paths and attributes defined by the calculation scenario thatare not required to respond to the query.
 7. A method as in claim 1,wherein at least one of the calculation nodes filters results obtainedfrom the database server.
 8. A method as in claim 1, wherein at leastone of the calculation nodes sorts results obtained from the databaseserver.
 9. A method as in claim 1, wherein the calculation scenario isinstantiated in a calculation engine layer by a calculation engine. 10.A method as in claim 9, wherein the calculation engine layer interactswith a physical table pool and a logical layer, the physical table poolcomprising physical tables containing data to be queried, and thelogical layer defining a logical metamodel joining at least a portion ofthe physical tables in the physical table pool.
 11. A method as in claim9, wherein the calculation engine invokes an SQL processor for executingset operations.
 12. A method as in claim 1, wherein an input for eachcalculation node comprises one or more of: a physical index, a joinindex, an OLAP index, and another calculation node.
 13. A method as inclaim 12, wherein each calculation node has at least one output tablethat is used to generate the data set.
 14. A method as in claim 13,wherein at least one calculation node consumes an output table ofanother calculation node.
 15. A method as in claim 1, wherein theexecuting comprises: forwarding the query to a calculation node in thecalculation scenario that is identified as a default node if the querydoes not specify a calculation node at which the query should beexecuted.
 16. A method as in claim 1, wherein the calculation scenariocomprises database metadata.
 17. A method as in claim 1, wherein thequery is a query of a composite data provider.
 18. A system comprising:a database server comprising memory and at least one data processor; anapplication server in communication with and remote from the databaseserver comprising memory and at least one data processor; wherein thedatabase server: receives a query associated with a calculation scenariothat defines a data flow model that includes one or more calculationnodes and that includes at least one hierarchy filter, each hierarchyfilter being a filter of at least one hierarchy table, each calculationnode defining one or more operations to execute on the database server;instantiates the calculation scenario, the instantiating optimizing thecalculation scenario, the instantiation comprising: transforming atleast one hierarchy filter to an inlist filter, the inlist filterrestricting values of a first join attribute to values of a second joinattribute after executing one join partner; and pushing down the inlistfilter to a lower calculation node; executes the operations defined bythe calculation nodes of the instantiated calculation scenario to resultin a responsive data set; and provides the data set to the applicationserver.
 19. A non-transitory computer program product storinginstructions, which when executed by at least one data processor of atleast one computing system, result in operations comprising: receiving aquery associated with a calculation scenario that defines a data flowmodel that includes one or more calculation nodes and that includes atleast one hierarchy filter, each hierarchy filter being a filter of atleast one hierarchy table, each calculation node defining one or moreoperations to execute on the database server; instantiating, by thedatabase server, the calculation scenario, the instantiating optimizingthe calculation scenario, the instantiating comprising: transforming atleast one hierarchy filter to an inlist filter, the inlist filterrestricting values of a first join attribute to values of a second joinattribute after executing one join partner; and pushing down the inlistfilter to a lower calculation node; executing the operations defined bythe calculation nodes of the optimized calculation scenario to result ina responsive data set; and providing the data set.